PyData Seattle 2015Swarm intelligence (SI) algorithms mimic the collective behavior of groups such as flocks of birds and schools of fish. This session describes in detail three major SI algorithms: amoeba method optimization, particle swam optimization, and simulated bee colony optimization. Attendees will receive Python source code for each algorithm.

Although SI algorithms have been studied for years, there is little practical implementation guidance available. This session describes the scenarios when SI algorithms are useful (and scenarios when SI algorithms are not useful), carefully explains how three major SI algorithms work, and presents a production quality, working demo, coded using Python, of each algorithm. Attendees will leave this session with a clear understanding of exactly what SI algorithms are, and have the knowledge needed to apply them immediately.

This session assumes attendees have intermediate or higher level coding ability with Python, but does not assume any knowledge of swarm intelligence.

The field of artificial intelligence is huge. One part of it is swarm intelligence. The collective intelligence of swarms and how theybehave and how that can be applied to real life autonomous robots. This video shows and explains traits of swarms and how those can be appliedin autonomous robots and real life situations where that is needed. To create this video I used parts from video documentaries fromBBC about different types of swarms (bats and ants) and videos from different artificial intelligence symposiums. All the rights belongto their respective owners. Terminology that is used at the start of the video was taken from the book "Swarm Intelligence: From Natural to Artificial Systems" by Eric Bonabeau and Marco DorigoAll copyrights and trademarks belong to their respective owners. No profit is made from this video, no copyright infringement intented

Tom Seeley, a professor of biology at Cornell University, compares the decision-making processes of honey bee colonies to human societies. When a colony swarms, scout bees fly to potential new hive sites and report their findings to the rest of the colony by performing what researchers call the "waggle dance." Their movements telegraph information about the location of sites they recommend to the rest of the group. Only after a number of scouts have visited the sites and performed their own dances in favor of the best site does the group decide to move to that site.

The video shows several of the real-robot experiments conducted at BRL (Bristol Robotics Lab) and included in the journal paper: "Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach" published in Swarm Intelligence (http://link.springer.com/article/10.1007/s11721-015-0117-7)

In this video, I discuss the basic concepts of grouping / flocking AI. I briefly cover the three basic steering behaviors: Separation, Cohesion, and Alignment. Combining all three of these results in Flocking AI.

NOTE: This video is a general overview of all three behaviors. When we get to each behavior's dedicated video, we will discuss the algorithm and logic more in-depth.

http://www.freegamesexplorer.com/games/videos/swarm-control/A tower defense game wherein you grow and command a hive of bees. Whenever you finish a level you may unlock new bee types and you earn flowers to buy defensive counter measures. The controls are quite easy, just drag and drop bees to target bears. Continuously train new bees and an attack incoming bear, some micromanagement is required when playing this game. The game starts off pretty easy and gets more challenging along the way. The concept is simple but it is sure to release a sense of euphoria for gamers who love to micromanage their way to victory.

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Harvard University scientists have developed a thousand tiny robots that, like swarming bees or army ants, can work together in vast numbers without a guiding central intelligence, in the largest team so far of self-organizing mobile robots. Photo: Michael Rubenstein, Harvard University.

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Starcraft's Zerg - some of the most iconic, yet misunderstood aliens in all of Sci-Fi history. We tend to despise these locusts of mass obedience - viewing them as a plague upon our individual freedoms. But what if I were to tell you we have more in common with that swarm than you even realize? Get ready to view the Zerg in a whole new light!

Thank you to Blizzard Entertainment for sponsoring this video.

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The following video is a high definition version of one experimental run performed with 20 e-pucks. In this experiment, the goal of the swarm is to reach a consensus over which color is the most spread on the arena surface. Robots' LEDs are red when favouring the black color and blue when favouring the white color.

G. Valentini, D. Brambilla, H. Hamann, M. Dorigo. "Collective Perception of Environmental Features in a Robot Swarm". In Proceedings of the Tenth International Conference on Swarm Intelligence, ANTS 2016

IMPORTANT UPDATE NOTICE: Due to the popularity and High Demand of Swarm Intelligence Software, ALL Copies of Swarm App have been Sold Out and occupied. Swarm-Intelligence.co will no longer be accepting new memberships for the time being. However if you're a newcoming trader & looking for a Trusted & Reliable Autotrader, I personally recommend looking into the snapcash binary software or wikitrader. They are automated systems just recently launched & completely upgraded, providing manual signals should which you can also trade yourself if you choose, and its based on actual technical & fundamental analysis for placing trades. These methods are exactly the type strategies used among financial trades, however they've simplified the process for rookie trades with an easy-to-use software named the SnapCash Binary app & WikiTrader. They also offer various settings you can control and features not seen with most softwares.You can check out their web pages here:

Note - there is no soundPlease watch with captions turned on!This video is a demonstration for a program I wrote for my Swarm Intelligence and Artificial Life Module at uni.It demonstrates the boids technique for simulating flocking in birds, implemented here in 2D using Microsoft's XNA.The boids algorithm was created by Craig Reynolds in 1986.http://www.red3d.com/cwr/boids/My version is roughly based on the pseudocode by Conrad Parkerhttp://www.kfish.org/boids/pseudocode.html

For more information please see my uni webpage about boids and flocking simulation at http://rs006913.webs.sse.reading.ac.uk/

To download the program and the source code:http://rs006913.webs.sse.reading.ac.uk/SwarmIntelligence/?page_id=48

OpenGL Swarm Intelligence - a simulation of a fish swarm from Daniel Schwamm. Written in Delphi 7 and OpenGL (DelphiGL). Every fish follows only some simple rules: (1) Rotate and concentrate to he middle (2) Escape from density (3) Escape from enemy (viewer and bombs) (4) Synchronize with fishes next to you. Full source code and tutorial (in German) can found under URL: http://www.daniel-schwamm.de/index.php?pg=delphi-tutorials/opengl-swarm-intelligence

Hey, I'm Orbital Potato and welcome to my Channel. Here I play a whole variety of games, specifically I enjoy playing Sci-fi, Simulation and Strategy games. If you have enjoyed this video, then please leave a like. If you want to see more then it would be awesome if you could subscribe. Also feel free to comment in the comments down bellow, I always appreciate the feedback.

My commentary audio kinda sucks not only because I wasn't all that articulate, but my computer needs a housefan that was coming in at only 30dB quieter than my voice (I filtered it out, but when you need to cancel that much noise, it shows up in the voices.) If you need subtitles, let me know!

This is an implementation of a genetic algorithm on a neural network. The "fighters" are capable of self-improvement in order to become stronger.

As you can see, the "fighters" are learning how to fight. Each one of them can see the bullets and the enemy in their vision field (represented by two lines), and have 5 possible actions : move forward, turn right, turn left, shoot and adjust their field of view.

For more details about the neural network, the programming, click here : http://doublezoom.free.fr/programmation/AG_Exemple_Fighting.php (french)

This program was made possible thanks to Pierre Lataillade and Maxime Gardet.

This program was done in lua, on my iPad, thanks to codea (http://twolivesleft.com/Codea/)The music, Five Armies by Kevin MacLeod is licensed under a CC Attribution 3.0.

http://www.gamezplay.org - Independent games channel - Over 9000 Zombies! is a retro style, top down, zombie shoot 'em up! Tons of AI enemies generated by Zombie Swarm Intelligence launch themselves in marauding hordes of fiendish pests for insane zombie killing action! Choose from a massive array of weapons, build a base using automated turrets and other fortifications and survive as long as you can while killing as many zombies as possible! The game also features co-operative multiplayer so you can play alongside your friends as you fight off hordes of zombies!

The game is still in Early Access, so some features might not yet be completely fine-tuned. Single player is well established, including 20+ weapons and 6 defensive turrets to help battle the unending hordes. Two player online co-op is also in, although this is still very early, so you can expect a few bugs with this initially.

PSO algorithm was used for swarm to find route to player. I know there are better algorithms for path finding, but for such type of an enemy like "ghosts" swarm I find it quite useful. It doesn't look so strange like it did when enemies were humanoids.

Algorithm update time and swarm factor are dynamic parameters, possible to change during runtime

A group of users harness their COLLECTIVE INTELLIGENCE using the UNUM platform and correctly predict not just the winners, but beat the spreads, in all sweet-sixteen games. Here is a replay of one collaborative prediction. For more info, visit: www.unanimousai.com

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